

import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 					__lowercase  (						unittest.TestCase				):





							"""simple docstring"""

							@property
							def       snake_case			(					self						)		->       Optional[Any]:
														torch.manual_seed(0						)
														A      :					Dict      		=					UNetaDModel(
														    block_out_channels=(32, 64)    ,						layers_per_block=2    ,						sample_size=32    ,						in_channels=3    ,						out_channels=3    ,						down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')    ,						up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')    ,						)
														return model






							def       snake_case			(					self						)		->       List[Any]:
														A      :					Any      		=					self.dummy_uncond_unet
														A      :					Tuple      		=					KarrasVeScheduler()

														A      :					Dict      		=					KarrasVePipeline(unet=__UpperCAmelCase    ,						scheduler=__UpperCAmelCase						)
														pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

														A      :					str      		=					torch.manual_seed(0						)
														A      :					Any      		=					pipe(num_inference_steps=2    ,						generator=__UpperCAmelCase    ,						output_type='''numpy'''						).images

														A      :					List[Any]      		=					torch.manual_seed(0						)
														A      :					Optional[int]      		=					pipe(num_inference_steps=2    ,						generator=__UpperCAmelCase    ,						output_type='''numpy'''    ,						return_dict=__UpperCAmelCase						)[0]

														A      :					Union[str, Any]      		=					image[0, -3:, -3:, -1]
														A      :					List[Any]      		=					image_from_tuple[0, -3:, -3:, -1]

														assert image.shape == (1, 32, 32, 3)
														A      :					str      		=					np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]						)

														assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
														assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1E-2







@slow
@require_torch
class 					__lowercase  (						unittest.TestCase				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       Tuple:
														A      :					Any      		=					'''google/ncsnpp-celebahq-256'''
														A      :					Optional[int]      		=					UNetaDModel.from_pretrained(__UpperCAmelCase						)
														A      :					Optional[int]      		=					KarrasVeScheduler()

														A      :					List[str]      		=					KarrasVePipeline(unet=__UpperCAmelCase    ,						scheduler=__UpperCAmelCase						)
														pipe.to(__UpperCAmelCase						)
														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

														A      :					Any      		=					torch.manual_seed(0						)
														A      :					List[str]      		=					pipe(num_inference_steps=20    ,						generator=__UpperCAmelCase    ,						output_type='''numpy'''						).images

														A      :					Dict      		=					image[0, -3:, -3:, -1]
														assert image.shape == (1, 2_56, 2_56, 3)
														A      :					Any      		=					np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]						)

														assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2









from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
		import timm


if is_torch_available():
		from torch import Tensor







class 					__lowercase  (						_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		int								=	'''pixel_values'''
							UpperCAmelCase_						:		List[str]								=	False
							UpperCAmelCase_						:		Tuple								=	TimmBackboneConfig




							def __init__(					self    ,						__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       int:
														requires_backends(self    ,						'''timm'''						)
														super().__init__(__UpperCAmelCase						)
														A      :					List[str]      		=					config

														if config.backbone is None:
																					raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''						)

														if config.backbone not in timm.list_models():
																					raise ValueError(f'backbone {config.backbone} is not supported by timm.'						)

														if hasattr(__UpperCAmelCase    ,						'''out_features'''						) and config.out_features is not None:
																					raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''						)

														A      :					str      		=					getattr(__UpperCAmelCase    ,						'''use_pretrained_backbone'''    ,						__UpperCAmelCase						)
														if pretrained is None:
																					raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''						)

														# We just take the final layer by default. This matches the default for the transformers models.
														A      :					Optional[int]      		=					config.out_indices if getattr(__UpperCAmelCase    ,						'''out_indices'''    ,						__UpperCAmelCase						) is not None else (-1,)

														A      :					str      		=					timm.create_model(
														    config.backbone    ,						pretrained=__UpperCAmelCase    ,						features_only=config.features_only    ,						in_chans=config.num_channels    ,						out_indices=__UpperCAmelCase    ,						**__UpperCAmelCase    ,						)
														# These are used to control the output of the model when called. If output_hidden_states is True, then
														# return_layers is modified to include all layers.
														A      :					str      		=					self._backbone.return_layers
														A      :					Any      		=					{layer['''module''']: str(__UpperCAmelCase						) for i, layer in enumerate(self._backbone.feature_info.info						)}
														super()._init_backbone(__UpperCAmelCase						)




							@classmethod
							def       snake_case			(					cls    ,						__UpperCAmelCase    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Any:
														requires_backends(cls    ,						['''vision''', '''timm''']						)
														from ...models.timm_backbone import TimmBackboneConfig

														A      :					Optional[Any]      		=					kwargs.pop('''config'''    ,						TimmBackboneConfig()						)

														A      :					str      		=					kwargs.pop('''use_timm_backbone'''    ,						__UpperCAmelCase						)
														if not use_timm:
																					raise ValueError('''use_timm_backbone must be True for timm backbones'''						)

														A      :					Optional[int]      		=					kwargs.pop('''num_channels'''    ,						config.num_channels						)
														A      :					List[str]      		=					kwargs.pop('''features_only'''    ,						config.features_only						)
														A      :					Optional[int]      		=					kwargs.pop('''use_pretrained_backbone'''    ,						config.use_pretrained_backbone						)
														A      :					Optional[int]      		=					kwargs.pop('''out_indices'''    ,						config.out_indices						)
														A      :					int      		=					TimmBackboneConfig(
														    backbone=__UpperCAmelCase    ,						num_channels=__UpperCAmelCase    ,						features_only=__UpperCAmelCase    ,						use_pretrained_backbone=__UpperCAmelCase    ,						out_indices=__UpperCAmelCase    ,						)
														return super()._from_config(__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       Any:
														pass






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=None    ,						**__UpperCAmelCase						)		->       Union[BackboneOutput, Tuple[Tensor, ...]]:
														A      :					Optional[int]      		=					return_dict if return_dict is not None else self.config.use_return_dict
														A      :					str      		=					(
														    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
														)
														A      :					Any      		=					output_attentions if output_attentions is not None else self.config.output_attentions

														if output_attentions:
																					raise ValueError('''Cannot output attentions for timm backbones at the moment'''						)

														if output_hidden_states:
																					# We modify the return layers to include all the stages of the backbone
																					A      :					Dict      		=					self._all_layers
																					A      :					int      		=					self._backbone(__UpperCAmelCase    ,						**__UpperCAmelCase						)
																					A      :					Any      		=					self._return_layers
																					A      :					Union[str, Any]      		=					tuple(hidden_states[i] for i in self.out_indices						)
														else:
																					A      :					Optional[int]      		=					self._backbone(__UpperCAmelCase    ,						**__UpperCAmelCase						)
																					A      :					List[Any]      		=					None

														A      :					str      		=					tuple(__UpperCAmelCase						)
														A      :					int      		=					tuple(__UpperCAmelCase						) if hidden_states is not None else None

														if not return_dict:
																					A      :					Optional[Any]      		=					(feature_maps,)
																					if output_hidden_states:
																												A      :					Tuple      		=					output + (hidden_states,)
																					return output

														return BackboneOutput(feature_maps=__UpperCAmelCase    ,						hidden_states=__UpperCAmelCase    ,						attentions=__UpperCAmelCase						)






